On the capacity side, the function class can represent so many hypotheses that there will exist many of them which fit the data, some based on spurious correlations rather than on the underlying human decision making process that generated those motions. The learner will not be able to disambiguate among them, and can converge to a hypothesis based on a correlate. Over-parametrization increases the ability to represent such hypotheses, lowering average error but possibly increasing error for tail events [1]}.
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